The ability to localize a road vehicle accurately within a given map is of crucial importance to autonomous operation. Typically this is done with a sensor payload of varying modalities, including scanning 3D LIDAR (the Velodyne HDL-64E, for example) and stereo cameras. In this talk I will present a localization approach that utilizes fixed scanning LIDARs to provide accurate vehicle pose estimates in real-world environments over the long-term. We utilize a retrospective windowing algorithm over run-time LIDAR data to build a dense history, that is subsequently matched within a 3D prior map to generate pose estimates. We show that this approach can effectively produce pose estimates within the prior map that are robust to long-term scene change and short-term transients.